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introduction

“Macroeconomic forecasts are often made by separating two objectives: providing the growth rate of the variable of interest (usually GDP) and detecting turning points in the economic cycle.” - Doz, Ferrara, and Pionnier, 2020

two models:
- linear regression: point estimates of future GDP
- random forest: using gdp and other variables to predict binary economic state

recession

“The NBER does not define a recession in terms of two consecutive quarters of decline in real GDP. Rather, a recession is a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales” - NBER, 2010

the random forest learns which months are contractionary as defined by NBER.

regression data datasets

• Probability of Losing a Job
  
• Job Openings per Unemployed Person
  
• Ratio of Hires to Openings
  
• Business Survey
  
• Yield Curve
  
• Economic Policy Index
  
• Equity Uncertainty
  
• Unemployment Rate (YoY)

random forest datasets

• gdp
  
• amt. of electricity generated (monthly) across lower 48 states
  
• number of active oil and gas rigs in the US
  
• long-term (>= 15weeks) unemployment rate
  
• US manufacturing industry confidence indicator
  
• yield curve

data sources

• Energy Information Administration
  
• Federal Reserve
  
• Organization for Economic Co-operation and Development
  
• National Bureau of Economic Research
  
• IHS Markit

annualized gdp change forecast

looking ahead based on February 2020 data:

Month	One Month	Two Months	Three Months
March 2020	6.27%	1.4%	2.62%
April 2020	NA	5.0%	2.02%
May 2020	NA	NA	3.37%

shiny x gdp forecasting

Number of Months:

1 2 3 4 5 6

Train-Test Split:

dull side of things

lagged_predictors <- function(n_lags, predictor_df, n_predictors=10){
  pred_df <- sapply(1:n_predictors, function(x){
    lag(predictor_df[,x],n_lags) })
  pred_df <- as.data.frame(pred_df)
  return(pred_df)}
predictors <- readRDS("./models/predictors.RDS")
predictors_oml <- lagged_predictors(as.numeric(input$n_lags),predictors)
predictors_oml <- cbind(predictors$GDP_LOG, predictors_oml) %>% drop_na()
testIDs <- sample(1:dim(predictors_oml)[1], 
                  input$train_test_adjust*dim(predictors_oml)[1])
training_predictors_oml <- predictors_oml[testIDs,]
testing_predictors_oml <- predictors_oml[-testIDs,]
gdp_oml_lm <- lm(GDP_LOG ~., data = training_predictors_oml)
pred_gdp_log_oml <- predict.lm(gdp_oml_lm, 
                               newdata = testing_predictors_oml)

growing the amazon

trctrl <- trainControl(method = "repeatedcv", 
                       number = 10, repeats = 5, search = "grid")
mtry <- ncol(train_x)
ntrees <- 101
tunegrid <- expand.grid(.mtry = c(2:mtry))
metric <- "Accuracy"
cl <- makePSOCKcluster(5)
registerDoParallel(cl)
rf_recession <- train(x = train_x, y = train_y, method = "rf", 
                      metric = metric, trControl = trctrl, 
                      tuneGrid = tunegrid, ntree = ntrees)
stopCluster(cl)

beneath the canopy

left-split <= s; right-split >s where s is split

results

with a 70-30 training-test split:

confusion matrix | pred (v), ref (h)

	N	Y
N	120	1
Y	2	13

random forest performance

Precision	Recall	F1	Prevalence	Accuracy
0.9917355	0.9836066	0.9876543	0.8970588	0.9779412

probabilistically speaking

pushing it just a little farther

confusion matrix | pred (v), ref (h)

	N	Y
N	120	3
Y	2	10

random forest performance

Precision	Recall	F1	Prevalence	Accuracy
0.9756098	0.9836066	0.9795918	0.9037037	0.962963

probabilistically speaking again

bringing it to the present

Econ. State	April 2020	May 2020
Prob. Expansion	86.14%	92.08%
Prob. Contraction	13.86%	7.92%

questions

source code: github.com/westerleyy/recession_predictR